An instance of a conventional method for speech recognition reference pattern adaptation is described in Patent Publication 1. A conventional apparatus for speech recognition reference pattern adaptation 200, shown in FIG. 6, includes a speech feature input section 201, a reference pattern memory 202, a speech recognition means 203, a reliability factor calculating means 204, a reference pattern adaptation means with the reliability factor 205, and an adaptation pattern memory 206.
The operation of the apparatus for speech recognition reference pattern adaptation 200, arranged as described above, is now explained. A string of feature amount of speech, used for adaptation, is delivered to the speech feature amount input section 201. The speech recognition means 203 recognizes this string of feature amount as speech, using reference patterns stored in the reference pattern memory 202, and outputs the results of recognition. The reliability factor calculating means 204 calculates the factor of reliability for the case of using the results of recognition as a teacher label of the input feature amount string.
The reference pattern adaptation means with reliability factor 205 accords weight, depending on the factor of reliability, using the feature amount string, teacher label and the reliability factor, for adaptation of the reference patterns to the input string of features.
The reference pattern adaptation means with reliability factor 205 is now explained.
In case the reference pattern is a Hidden Markov Model, referred to below as HMM, now in extensive use for speech recognition, and a mean vector of the Gaussian distribution is adapted as HMM parameter, an input feature amount string at time t=1, 2, . . . , TO=[o(1),o(2), . . . , o(T)]  [Equation 1]a Gaussian distribution sequence, obtained by the reliability factor calculating means 204, equation 2:L=[l(1),l(2), . . . , l(T)]  [Equation 2]which is of the highest likelihood as a teacher label and a reliability factorS=[s(1),s(2), . . . , s(T)]  [Equation 3]are used to calculate, for a label q=l(t) at time t, the adaptation data corrected by weighting that makes use of the reliability factor, as shown by the equation (4):
                                                        o              ′                        ⁡                          (                              t                ,                q                            )                                =                                                                      s                  ⁡                                      (                    t                    )                                                                    τ                  +                                      s                    ⁡                                          (                      t                      )                                                                                  ⁢                              o                ⁡                                  (                  t                  )                                                      +                                          τ                                  τ                  +                                      s                    ⁡                                          (                      t                      )                                                                                  ⁢                              μ                ⁡                                  (                  q                  )                                                                    ,                  q          =                      1            ⁢                          (              t              )                                                          [                  Equation          ⁢                                          ⁢          4                ]            where μ(q) denotes a mean vector of the Gaussian distribution for the label q, and τ is a control constant having a value not smaller than 0.
By adapting the reference pattern, with the use of the so corrected adaptation data, it is possible to reduce the effect of speech data having a low reliability factor and to calculate an adaptation pattern in which the adverse effect of the error in the teacher label, that is, the error in the results of recognition, is diminished.
As an instance of the method for forming a correct solution teacher label, the Viterbi algorithm is indicated in Non-Patent Publication 1.
As examples of techniques for reference pattern adaptation, MAP (Maximum A Posteori) adaptation. MLLR (Maximum Likelihood Linear Regression) adaptation, AMCC (Autonomus Model Complexity Control) adaptation and EigenVoice adaptation are disclosed in Non-Patent Publication 2. On the other hand, HEV (Hierarchical EigenVoice) adaptation is disclosed in Non-Patent Publication 3.    Patent Publication 1: JP Patent 3589044    Non-Patent Publication 1: Rabiner B and H. Juang, Foundation of Speech Recognition, NTT Advance Technology KK, 1995    Non-Patent Publication 2: Koichi SHINODA, Speaker Adaptation Techniques for Speech Recognition Using Probabilistic Models, IEICE, 2004, Vol. J87-D-II, No. 2, pp. 371-386    Non-Patent Publication 3: Y. Onishi and K. Iso, “Speaker Adaptation by Hierarchical Eigenvoice”, Proc. ICASSP-2003, pp. I-576-579, 2003